AntTree: a new model for clustering with artificial ants

We present a new clustering algorithm for unsupervised learning. It is inspired from the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. The artificial ants that we have defined similarly builds a tree. Each ant represents one data. The way ants move and build this tree depends on the similarity between the data. We have compared our results to those obtained by the k-means algorithm and by AntClass on numerical databases (either artificial, real, or from the CE.R.I.E.S.). We show that AntTree significantly improves the clustering process.